Results 1 to 10 of about 4,815 (240)
LSTM in Algorithmic Investment Strategies on BTC and S&P500 Index [PDF]
Sensors, 2022 We use LSTM networks to forecast the value of the BTC and S&P500 index, using data from 2013 to the end of 2020, with the following frequencies: daily, 1 h, and 15 min data. We introduce our innovative loss function, which improves the usefulness of the forecasting ability of the LSTM model in algorithmic investment strategies.
Jakub Michanków +2 more
europepmc +5 more sources
Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index [PDF]
Entropy, 2022 This research aims to compare the performance of ARIMA as a linear model with that of the combination of ARIMA and GARCH family models to forecast S&P500 log returns in order to construct algorithmic investment strategies on this index. We used the data collected from Yahoo Finance with daily frequency for the period from 1 January 2000 to 31 ...
Nguyen Vo, Robert Slepaczuk
europepmc +6 more sources
LSTM-ARIMA AS A HYBRID APPROACH IN ALGORITHMIC INVESTMENT STRATEGIES
Working PapersThis study focuses on building an algorithmic investment strategy employing a hybrid approach that combines LSTM and ARIMA models referred to as LSTM-ARIMA. This unique algorithm uses LSTM to produce final predictions but boost results of this RNN by adding the residuals obtained from ARIMA predictions among other inputs. The algorithm is tested across
Kamil Kashif, Robert Slepaczuk
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A deep learning algorithm for optimal investment strategies
, 2021 This paper treats the Merton problem how to invest in safe assets and risky assets to maximize an investor's utility, given by investment opportunities modeled by a $d$-dimensional state process. The problem is represented by a partial differential equation with optimizing term: the Hamilton-Jacobi-Bellman equation. The main purpose of this paper is to
Gim, Daeyung, Park, Hyungbin
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Information and algorithmic support of a multi-level integrated system for the investment strategies formation [PDF]
3rd International Workshop on Information, Computation, and Control Systems for Distributed Environments 2021, 2021 The article summarizes the accumulated practical experience of the authors in the development of algorithms for the formation of investment strategies. For this purpose, the optimization of the studied parameters, information support of investment activities, verification, monitoring and adjustment in the testing mode and the subsequent practical ...
David Gercekovich +4 more
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Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data
Working PapersThe article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Mean Absolute Directional Loss (GMADL) and Quantile loss, are proposed and evaluated against the Buy ...
Filip Stefaniuk, Robert Slepaczuk
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BCP Business & Management, 2022 Gold and Bitcoin are two common investments in the market. Investors profit from the capital market through specific trading strategies. Based on previous price data, this paper established a prediction-decision model, which provides investors with an optimal trading strategy and effectively improves the return on investment.
Yuwei Chen +4 more
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Creating Investment Strategies Based on Machine Learning Algorithms
Construction de stratégies d'investissement en utilisant des algorithmes de Machine Learning La thèse a permis de développer deux stratégies d'investissement issues d'algorithmes de Machine Learning:La première (Low Turbulence Model) est une stratégie sur indice qui combine à la fois du traitement du signal et des modèles graphiques ...
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Various Approaches to Algorithmic Investment Strategies on Equity Indices and Stocks Markets
, 2021 AT_PUBLICATION
Naumowicz, Piotr +2 more
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Research on Investment Strategies Considering Investors' Emotions Based on Optimized CNN Algorithm
, 2023 : This paper explores how to extract investor sentiment in text using an optimized convolutional neural network algorithm and form investor opinions based on it, so as to construct a portfolio model with a higher rate of return. Specifically, based on the text data of SSE 50 constituent stocks in the Oriental Fortune stock bar, the optimized ...
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